class: center, middle, inverse, title-slide # Introduction to R for Data Analysis ## Data Types, Import & Export ### Johannes Breuer & Stefan Jünger ### 2021-08-02 --- layout: true --- ## Getting data into `R` Thus far, we've already learned what `R` and `RStudio` are. This course is about starting to use `R` and feeling prepared to use it for statistical analyses. There's one essential prerequisite: .center[**We need data!**] <img src="data:image/png;base64,#C:\Users\breuerjs\Documents\Lehre\r-intro-gesis-2021\content\img\import_data.png" width="50%" style="display: block; margin: auto;" /> --- ## Content of this session - What are `R`'s internal data types? - How to work with different data types? - How to import data in different formats? - How to export data in different formats --- ## Data we use in this course During the course, we use several different datasets. Mainly in this session, where we apply different importing functions, we use a large variety ranging from data about the Titanic to data about unicorns. However, we will also use data that are (presumably) more interesting for social and behavioral scientists. --- ## It boils down to... .pull-left[ **How your data are stored (data types)** - 'Numbers' (Integers & Doubles) - Character Strings - Logical - Factors - ... - There's more, e.g., expressions, but let's leave it at that ] .pull-right[ **Where your data are stored (data formats)** - Vectors - Matrices - Arrays - Data frames / Tibbles - Lists ] .footnote[https://www.stat.berkeley.edu/~nolan/stat133/Fall05/lectures/DataTypes4.pdf] --- ## Numeric data .small[ *Integers* are values without a decimal value. To be explicit in `R` in using them, you have to place an `L` behind the actual value, just like that: ```r 1L ``` ``` ## [1] 1 ``` By contrast, *doubles* are values with a decimal value. ```r 1.1 ``` ``` ## [1] 1.1 ``` We can check data types by using the `typeof()` function. ```r typeof(1L) ``` ``` ## [1] "integer" ``` ```r typeof(1.1) ``` ``` ## [1] "double" ``` ] --- ## Character strings At first glance, a *character* is a letter somewhere between a-z. *String* in this context might mean that we have a series of characters. However, numbers and other symbols can be part of a *character string*, which can then be, e.g., part of a text. In `R`, character strings are wrapped in quotation marks. ```r "Hi. I am a character string, the 1st of its kind!" ``` ``` ## [1] "Hi. I am a character string, the 1st of its kind!" ``` So character strings are meaningless, which means that there are no values associated with their content unless we change that, e.g., with factors. --- ## Factors If you're a *Stata* (or *SPSS*) user, you may already be familiar with factors. Factors are data types that assume that their values are not continuous, e.g., as in ordinal or nominal data. ```r factor(1.1) ``` ``` ## [1] 1.1 ## Levels: 1.1 ``` ```r factor("Hi. I am a character string, the 1st of its kind!") ``` ``` ## [1] Hi. I am a character string, the 1st of its kind! ## Levels: Hi. I am a character string, the 1st of its kind! ``` Factors take numeric data or character strings as input as they simply convert them into so-called levels. This concept may be a little bit abstract for the time being. It's just essential to have heard about them before you learn more about them. --- ## Logical values Logical values are basically either `TRUE` or `FALSE` values. These values are produced by making logical requests on your data. ```r 2 > 1 ``` ``` ## [1] TRUE ``` ```r 2 < 1 ``` ``` ## [1] FALSE ``` I'd say that logical values are at the heart of creating loops. For this purpose, we need way more logical operators to request `TRUE` or `FALSE` values. --- ## Logical operators There are quite a few logical operators in `R`: .pull-left[ - `<` less than - `<=` less than or equal to - `>` greater than - `>=` greater than or equal to - `== ` exactly equal to - `!=` not equal to ] .pull-right[ - `!x` Not x - `x | y` x OR y - `x & y ` x AND y - `isTRUE(x)` test if X is TRUE - `isFALSE(x)` test if X is FALSE ] .footnote[https://www.statmethods.net/management/operators.html] Moreover, there are some more `is.PROPERTY_ASKED_FOR()` functions, such as `is.numeric()`, which also return `TRUE` or `FALSE` values. --- ## `R`'s data formats `R`'s different data types can be put into 'container's. <img src="data:image/png;base64,#C:\Users\breuerjs\Documents\Lehre\r-intro-gesis-2021\content\img\9213.1526125966.png" width="75%" style="display: block; margin: auto;" /> .footnote[https://devopedia.org/r-data-structures] --- ## Vectors Vectors are built by enclosing your content with `c()` ("c" for "concatenate") ```r numeric_vector <- c(1, 2, 3, 4) character_vector <- c("a", "b", "c", "d") numeric_vector ``` ``` ## [1] 1 2 3 4 ``` ```r character_vector ``` ``` ## [1] "a" "b" "c" "d" ``` Vectors are really like vectors in mathematics. Initially, it doesn't matter if you look at them as column or row vectors. --- ## ...but it matters when you combine vectors Using the function `cbind()` or `rbind()` you can either combine vectors column-wise or row-wise, respectively. ```r cbind(numeric_vector, character_vector) ``` ``` ## numeric_vector character_vector ## [1,] "1" "a" ## [2,] "2" "b" ## [3,] "3" "c" ## [4,] "4" "d" ``` ```r rbind(numeric_vector, character_vector) ``` ``` ## [,1] [,2] [,3] [,4] ## numeric_vector "1" "2" "3" "4" ## character_vector "a" "b" "c" "d" ``` They are now matrices (also numeric values are coerced into strings). --- ## Matrices Matrices are the basic rectangular data format in R. ```r fancy_matrix <- matrix(1:16, nrow = 4) fancy_matrix ``` ``` ## [,1] [,2] [,3] [,4] ## [1,] 1 5 9 13 ## [2,] 2 6 10 14 ## [3,] 3 7 11 15 ## [4,] 4 8 12 16 ``` You cannot store multiple data types, such as strings and numeric values in the same matrix. Otherwise, your data will get coerced to a common type, as seen in the previous slide. This is something that happens already within vectors: ```r c(1, 2, "evil string") ``` ``` ## [1] "1" "2" "evil string" ``` --- ## Data frames While matrices are used, e.g.,--\*drumroll\*-- for matrix operations, data frames resemble more the data formats most of you are probably already familiar with. We can build data frames by hand as here: .tinyish[ ```r library(randomNames) # a name generator package fancy_data <- data.frame( who = randomNames(n = 10, which.names = "first"), age = sample(14:49, 10, replace = TRUE), # you see what we are doing here? salary_2018 = sample(15:100, 10, replace = TRUE), salary_2019 = sample(15:100, 10, replace = TRUE) ) fancy_data ``` ] .right[↪️] --- class: middle ``` ## who age salary_2018 salary_2019 ## 1 Malcolm 18 40 79 ## 2 Amanda 46 64 46 ## 3 Sean 17 80 94 ## 4 Taju 22 97 29 ## 5 Justin 47 69 43 ## 6 Junaid 15 77 44 ## 7 Lavall 40 48 46 ## 8 Brianna 14 36 29 ## 9 Rebeca 41 37 92 ## 10 Christian 42 100 48 ``` --- ## Tibbles .pull-left[ Tibbles are basically just `R data.frames` but nicer. - only the first ten observations are printed - the output is tidier! - you get some additional metadata about rows and columns that you would normally only get when using `dim()` and other functions You can check the [tibble vignette](https://cran.r-project.org/web/packages/tibble/vignettes/tibble.html) for technical details. ] .pull-right[ <img src="data:image/png;base64,#C:\Users\breuerjs\Documents\Lehre\r-intro-gesis-2021\content\img\tibble.png" width="60%" style="display: block; margin: auto;" /> ] --- ## Tibble conversion ```r library(tibble) as_tibble(fancy_data) ``` ``` ## # A tibble: 10 x 4 ## who age salary_2018 salary_2019 ## <chr> <int> <int> <int> ## 1 Malcolm 18 40 79 ## 2 Amanda 46 64 46 ## 3 Sean 17 80 94 ## 4 Taju 22 97 29 ## 5 Justin 47 69 43 ## 6 Junaid 15 77 44 ## 7 Lavall 40 48 46 ## 8 Brianna 14 36 29 ## 9 Rebeca 41 37 92 ## 10 Christian 42 100 48 ``` --- ## One last type you should know: lists Lists are perfect for storing numerous and potentially diverse information in one place. ```r fancy_list <- list( numeric_vector, character_vector, fancy_matrix, fancy_data ) fancy_list ``` .right[↪️] --- class: middle .tinyish[ ``` ## [[1]] ## [1] 1 2 3 4 ## ## [[2]] ## [1] "a" "b" "c" "d" ## ## [[3]] ## [,1] [,2] [,3] [,4] ## [1,] 1 5 9 13 ## [2,] 2 6 10 14 ## [3,] 3 7 11 15 ## [4,] 4 8 12 16 ## ## [[4]] ## who age salary_2018 salary_2019 ## 1 Malcolm 18 40 79 ## 2 Amanda 46 64 46 ## 3 Sean 17 80 94 ## 4 Taju 22 97 29 ## 5 Justin 47 69 43 ## 6 Junaid 15 77 44 ## 7 Lavall 40 48 46 ## 8 Brianna 14 36 29 ## 9 Rebeca 41 37 92 ## 10 Christian 42 100 48 ``` ] --- ## Nested lists ```r fancy_nested_list <- list( fancy_vectors = list(numeric_vector, character_vector), data_stuff = list(fancy_matrix, fancy_data) ) fancy_nested_list ``` .right[↪️] --- class: middle .tinyish[ ``` ## $fancy_vectors ## $fancy_vectors[[1]] ## [1] 1 2 3 4 ## ## $fancy_vectors[[2]] ## [1] "a" "b" "c" "d" ## ## ## $data_stuff ## $data_stuff[[1]] ## [,1] [,2] [,3] [,4] ## [1,] 1 5 9 13 ## [2,] 2 6 10 14 ## [3,] 3 7 11 15 ## [4,] 4 8 12 16 ## ## $data_stuff[[2]] ## who age salary_2018 salary_2019 ## 1 Malcolm 18 40 79 ## 2 Amanda 46 64 46 ## 3 Sean 17 80 94 ## 4 Taju 22 97 29 ## 5 Justin 47 69 43 ## 6 Junaid 15 77 44 ## 7 Lavall 40 48 46 ## 8 Brianna 14 36 29 ## 9 Rebeca 41 37 92 ## 10 Christian 42 100 48 ``` ] --- ## Accessing elements by index Generally, there is this use of `[index_number]`-logic in `R` to access only a subset of information in data, no matter if we have vectors or data frames. Say, we want to extract the second element of our `character_vector` object, we could do that like this: ```r character_vector[2] ``` ``` ## [1] "b" ``` --- # More complicated cases: matrices Matrices can have more dimensions, often you want information from a specific row and column. ```r a_wonderful_matrix[number_of_row, number_of_column] ``` *Note*: You can do the same indexing with `data.frame`s. We will talk more about this in the session on *Data Wrangling Basics*. --- ## Matrices and subscripts (as in mathematical notation) Identify rows, columns, or elements using subscripts is similar to matrix notation: ```r fancy_matrix[, 4] # 4th column of matrix fancy_matrix[3,] # 3rd row of matrix fancy_matrix[2:4, 1:3] # rows 2,3,4 of columns 1,2,3 ``` It's really like in math, and you can perform standard mathematical operations, such as matrix multiplications. ```r fancy_matrix[2:4, 1:3] %*% fancy_matrix[1:3, 2:4] ``` ``` ## [,1] [,2] [,3] ## [1,] 116 188 260 ## [2,] 134 218 302 ## [3,] 152 248 344 ``` --- ## The case of data frames A nice feature of `data.frames` or `tibbles` is that their columns are names, just as variable names in ordinary data. It would be cumbersome do use index numbers to extract a specific column/variable, right? Do not fear: ```r fancy_data$who ``` ``` ## [1] "Malcolm" "Amanda" "Sean" "Taju" "Justin" "Junaid" "Lavall" "Brianna" "Rebeca" "Christian" ``` Just place a `$`-sign between the data object and the variable name. --- ## `[]` in data frames Sometimes we also have to rely on character strings as input information, e.g., for iterating over data. We can also use `[]` to access variables by name. .pull-left[ Not only this way: ```r fancy_data[1] ``` ``` ## who ## 1 Malcolm ## 2 Amanda ## 3 Sean ## 4 Taju ## 5 Justin ## 6 Junaid ## 7 Lavall ## 8 Brianna ## 9 Rebeca ## 10 Christian ``` ] .pull-right[ But also this way: ```r fancy_data["who"] ``` ``` ## who ## 1 Malcolm ## 2 Amanda ## 3 Sean ## 4 Taju ## 5 Justin ## 6 Junaid ## 7 Lavall ## 8 Brianna ## 9 Rebeca ## 10 Christian ``` ] --- ## Difference between `[]` and `[[]]` <blockquote class="twitter-tweet" data-width="550" data-lang="en" data-dnt="true" data-theme="dark"><p lang="en" dir="ltr">Indexing lists in <a href="https://twitter.com/hashtag/rstats?src=hash&ref_src=twsrc%5Etfw">#rstats</a>. Inspired by the Residence Inn <a href="http://t.co/YQ6axb2w7t">pic.twitter.com/YQ6axb2w7t</a></p>— Hadley Wickham (@hadleywickham) <a href="https://twitter.com/hadleywickham/status/643381054758363136?ref_src=twsrc%5Etfw">September 14, 2015</a></blockquote> --- ## Dataframe check 1, 2, 1, 2! Even before checking the codebook for a dataset (if there is one) it always helps to have a quick look at the data. The most high-level information you can get is about the object type and its dimensions. .small[ ```r # object type class(fancy_data) ``` ``` ## [1] "data.frame" ``` ```r # number of rows and columns dim(fancy_data) ``` ``` ## [1] 10 4 ``` ```r # number of rows nrow(fancy_data) ``` ``` ## [1] 10 ``` ```r # number of columns ncol(fancy_data) ``` ``` ## [1] 4 ``` ] --- ## Dataframe check 1, 2, 1, 2! You can also print the first 6 lines of the dataframe with `head()`. You can easily change the number of lines by providing the number as the second argument to the `head()` function. ```r head(fancy_data, 3) ``` ``` ## who age salary_2018 salary_2019 ## 1 Malcolm 18 40 79 ## 2 Amanda 46 64 46 ## 3 Sean 17 80 94 ``` --- ## Dataframe check 1, 2, 1, 2! If we want some more (detailed) information about the dataset or object, we can use the `base R` function `str()`. ```r str(fancy_data) ``` ``` ## 'data.frame': 10 obs. of 4 variables: ## $ who : chr "Malcolm" "Amanda" "Sean" "Taju" ... ## $ age : int 18 46 17 22 47 15 40 14 41 42 ## $ salary_2018: int 40 64 80 97 69 77 48 36 37 100 ## $ salary_2019: int 79 46 94 29 43 44 46 29 92 48 ``` --- ## Dataframe check 1, 2, 1, 2! As you saw on the previous slide, the output of `str()` can be a bit hard to read (especially for larger datasets). A good alternative that creates a more clearly laid output is the `glimpse()` function from the `dplyr` package. ```r glimpse(fancy_data) ``` ``` ## Rows: 10 ## Columns: 4 ## $ who <chr> "Malcolm", "Amanda", "Sean", "Taju", "Justin", "Junaid", "Lavall", "Brianna", "Rebeca", "Christian" ## $ age <int> 18, 46, 17, 22, 47, 15, 40, 14, 41, 42 ## $ salary_2018 <int> 40, 64, 80, 97, 69, 77, 48, 36, 37, 100 ## $ salary_2019 <int> 79, 46, 94, 29, 43, 44, 46, 29, 92, 48 ``` --- ## Dataframe check 1, 2, 1, 2! If you want to have a look at your full dataset, you can use the `View()` function. In *RStudio*, this will open a new tab in the source pane through which you can explore the dataset (including a search function). You can also click on the small spreadsheet symbol on the right side of the object in the environment tab to open this view. ```r View(fancy_data) ``` <img src="data:image/png;base64,#C:\Users\breuerjs\Documents\Lehre\r-intro-gesis-2021\content\img\rstudio_view.png" width="65%" style="display: block; margin: auto;" /> --- ## Receiving and defining names We can print all names of an object using the `names()` function... ```r names(fancy_data) ``` ``` ## [1] "who" "age" "salary_2018" "salary_2019" ``` ...and we can change its names with it. ```r names(fancy_data) <- c("name", "salary_2018", "salary_2019") names(fancy_data) ``` ``` ## [1] "name" "salary_2018" "salary_2019" NA ``` However, there are more flexible ways of doing this as we will see later. --- class: center, middle # [Exercise](https://jobreu.github.io/r-intro-gesis-2021/exercises/Exercise_1_2_1_Data_Types.html) time 🏋️‍♀️💪🏃🚴 ## [Solutions](https://jobreu.github.io/r-intro-gesis-2021/solutions/Exercise_1_2_1_Data_Types.html) --- ## GESIS Panel Data on the Coronavirus Outbreak .left-column[ <img src="data:image/png;base64,#C:\Users\breuerjs\Documents\Lehre\r-intro-gesis-2021\content\img\gesis_panel_logo_web.jpg" width="372" style="display: block; margin: auto;" /> ] .right-column[ For most of the examples and exercises in this course we will use the [Public Use File (PUF) of the GESIS Panel Special Survey on the Coronavirus SARS-CoV-2 Outbreak in Germany](https://www.gesis.org/gesis-panel/coronavirus-outbreak/public-use-file-puf). You can [download the dataset in different formats as well as the codebook and the questionnaire (in German) from the *GESIS* Data Archive](https://search.gesis.org/research_data/ZA5667) (note: you need to have/create a user account). The *GESIS Panel* website provides [detailed documentation](https://www.gesis.org/gesis-panel/documentation), including a [cheatsheet](https://www.gesis.org/fileadmin/upload/forschung/programme_projekte/Drittmittelprojekte/GESIS_Panel/gesis_panel_cheatsheet.pdf). ] --- ## Gapminder Data .left-column[ <img src="data:image/png;base64,#C:\Users\breuerjs\Documents\Lehre\r-intro-gesis-2021\content\img\gapminder_logo.png" width="1200" style="display: block; margin: auto;" /> ] .right-column[ We will also use [data from *Gapminder*](https://www.gapminder.org/data/). During the course and the exercises, we work with data we have downloaded from their website. There also is an `R` package that bundles some of the *Gapminder* data: `install.packages("gapminder")`. This `R` package provides ["[a]n excerpt of the data available at Gapminder.org. For each of 142 countries, the package provides values for life expectancy, GDP per capita, and population, every five years, from 1952 to 2007."](https://cran.r-project.org/web/packages/gapminder/index.html) ] --- ## How to use the data in general To code along and be able to do the exercises, you should store the data files for the *GESIS Panel Special Survey on the Coronavirus SARS-CoV-2 Outbreak in Germany* in a folder called `data` in the same folder as the other materials for this course. The *Gapminder* data (as well as the Titanic and unicorn) should already be in the `data` folder (if you downloaded/cloned the materials from the [*GitHub* repo for this course](https://github.com/jobreu/r-intro-gesis-2021)). We also provide you a synthetic data set based on the GESIS Panel data. This synthetic data set was created by [Bernd Weiß](https://berndweiss.net/) using the [`synthpop` package](https://www.synthpop.org.uk/). --- ## `R` is data-agnostic <img src="data:image/png;base64,#C:\Users\breuerjs\Documents\Lehre\r-intro-gesis-2021\content\img\Datenimport.PNG" width="65%" style="display: block; margin: auto;" /> --- ## But the choice of packages is intimidating .pull-left[ **What you will learn** - Getting the most common data formats into `R` - e.g., CSV, *Stata*, *SPSS*, or *Excel* spreadsheets - Using the most recent methods of doing that - We will rely a lot on packages and functions from the `tidyverse` instead of using `base R` ] .pull-right[ **What you won't learn** - Getting old & obscure binary data formats into `R` - ... although [it's possible](https://cran.r-project.org/doc/manuals/r-release/R-data.html) ] --- ## Before writing any code: *RStudio* functionality to import data `R` is no longer just for command line heroes. In the *RStudio* IDE menu, you can also select and load your data using the mouse. It's under `Environment - Import Dataset - Choose file type`. <img src="data:image/png;base64,#C:\Users\breuerjs\Documents\Lehre\r-intro-gesis-2021\content\img\rstudio_import.PNG" width="716" style="display: block; margin: auto;" /> --- ## Where to find data **Browse Button in `RStudio`** <img src="data:image/png;base64,#C:\Users\breuerjs\Documents\Lehre\r-intro-gesis-2021\content\img\importBrowse.PNG" width="75%" style="display: block; margin: auto;" /> **Code preview in `Rstudio`** <img src="data:image/png;base64,#C:\Users\breuerjs\Documents\Lehre\r-intro-gesis-2021\content\img\codepreview.PNG" width="75%" style="display: block; margin: auto;" /> --- ## Honestly, after some time you will write the code directly .center[ <img src="data:image/png;base64,#C:\Users\breuerjs\Documents\Lehre\r-intro-gesis-2021\content\img\coding_cat.gif" style="display: block; margin: auto;" /> ] .footnote[[Source](https://media.giphy.com/media/LmNwrBhejkK9EFP504/source.gif)] --- ## Honestly, after some time you will write the code directly .center[ <img src="data:image/png;base64,#C:\Users\breuerjs\Documents\Lehre\r-intro-gesis-2021\content\img\hadley-typing.gif" style="display: block; margin: auto;" /> ] [Source](https://tenor.com/view/hadley-wickham-rstats-typing-rcode-gif-11365139) --- ## Simple vs. not so simple file formats Basic file formats, such as CSV (comma-separated value file), can directly be imported into `R` - they are 'flat' - few metadata - basically text files Other file formats, particularly the proprietary ones, require the use of additional packages - they are complex - a lot of metadata (think of all the labels in an *SPSS* file) - they are binary (1110101) --- ## File formats are subject of war <img src="data:image/png;base64,#C:\Users\breuerjs\Documents\Lehre\r-intro-gesis-2021\content\img\norm_normal_file_format.png" width="30%" style="display: block; margin: auto;" /> https://xkcd.com/2116/ --- ## Personal sidenote: why `tidyverse` for importing? For simple files, `base R` provides proper tools for importing. Yet, for importing other files, we have to rely on additional packages anyway. - the `tidyverse` packages (and its "friends") allow us to import and export all kinds of different data formats in a coherent way - the tidy data format also facilitates adding metadata to imported data - they are tibbles - a specific kind are labelled data (more on that in a bit) - the `tidyverse` provides some sane defaults, e.g., by automatic data type detection --- ## Disclaimer **In the following slides, we'll jump right into importing data. We use a lot of different packages for this purpose, and you don't have to remember everything. It's just for making a point of how agnostic `R` actually is regarding the file type. Later on, we will dive more into the specifics of importing.** --- ## For starters: Importing a CSV file using `Base R` ```r titanic <- read.csv("./data/titanic.csv") titanic ``` .small[] ``` ## PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket ## 1 1 0 3 Braund, Mr. Owen Harris male 22.00 1 0 A/5 21171 ## 2 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38.00 1 0 PC 17599 ## 3 3 1 3 Heikkinen, Miss. Laina female 26.00 0 0 STON/O2. 3101282 ## 4 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.00 1 0 113803 ## 5 5 0 3 Allen, Mr. William Henry male 35.00 0 0 373450 ## 6 6 0 3 Moran, Mr. James male NA 0 0 330877 ## 7 7 0 1 McCarthy, Mr. Timothy J male 54.00 0 0 17463 ## 8 8 0 3 Palsson, Master. Gosta Leonard male 2.00 3 1 349909 ## 9 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.00 0 2 347742 ## 10 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14.00 1 0 237736 ## 11 11 1 3 Sandstrom, Miss. Marguerite Rut female 4.00 1 1 PP 9549 ## 12 12 1 1 Bonnell, Miss. Elizabeth female 58.00 0 0 113783 ## 13 13 0 3 Saundercock, Mr. William Henry male 20.00 0 0 A/5. 2151 ## 14 14 0 3 Andersson, Mr. Anders Johan male 39.00 1 5 347082 ## 15 15 0 3 Vestrom, Miss. Hulda Amanda Adolfina female 14.00 0 0 350406 ## 16 16 1 2 Hewlett, Mrs. (Mary D Kingcome) female 55.00 0 0 248706 ## 17 17 0 3 Rice, Master. Eugene male 2.00 4 1 382652 ## 18 18 1 2 Williams, Mr. Charles Eugene male NA 0 0 244373 ## 19 19 0 3 Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele) female 31.00 1 0 345763 ## 20 20 1 3 Masselmani, Mrs. Fatima female NA 0 0 2649 ## 21 21 0 2 Fynney, Mr. Joseph J male 35.00 0 0 239865 ## 22 22 1 2 Beesley, Mr. Lawrence male 34.00 0 0 248698 ## 23 23 1 3 McGowan, Miss. Anna "Annie" female 15.00 0 0 330923 ## 24 24 1 1 Sloper, Mr. William Thompson male 28.00 0 0 113788 ## 25 25 0 3 Palsson, Miss. Torborg Danira female 8.00 3 1 349909 ## 26 26 1 3 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson) female 38.00 1 5 347077 ## 27 27 0 3 Emir, Mr. Farred Chehab male NA 0 0 2631 ## 28 28 0 1 Fortune, Mr. Charles Alexander male 19.00 3 2 19950 ## 29 29 1 3 O'Dwyer, Miss. Ellen "Nellie" female NA 0 0 330959 ## 30 30 0 3 Todoroff, Mr. Lalio male NA 0 0 349216 ## 31 31 0 1 Uruchurtu, Don. Manuel E male 40.00 0 0 PC 17601 ## 32 32 1 1 Spencer, Mrs. William Augustus (Marie Eugenie) female NA 1 0 PC 17569 ## 33 33 1 3 Glynn, Miss. Mary Agatha female NA 0 0 335677 ## 34 34 0 2 Wheadon, Mr. Edward H male 66.00 0 0 C.A. 24579 ## 35 35 0 1 Meyer, Mr. Edgar Joseph male 28.00 1 0 PC 17604 ## 36 36 0 1 Holverson, Mr. Alexander Oskar male 42.00 1 0 113789 ## 37 37 1 3 Mamee, Mr. Hanna male NA 0 0 2677 ## 38 38 0 3 Cann, Mr. Ernest Charles male 21.00 0 0 A./5. 2152 ## 39 39 0 3 Vander Planke, Miss. Augusta Maria female 18.00 2 0 345764 ## 40 40 1 3 Nicola-Yarred, Miss. Jamila female 14.00 1 0 2651 ## 41 41 0 3 Ahlin, Mrs. Johan (Johanna Persdotter Larsson) female 40.00 1 0 7546 ## 42 42 0 2 Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott) female 27.00 1 0 11668 ## 43 43 0 3 Kraeff, Mr. Theodor male NA 0 0 349253 ## 44 44 1 2 Laroche, Miss. Simonne Marie Anne Andree female 3.00 1 2 SC/Paris 2123 ## 45 45 1 3 Devaney, Miss. Margaret Delia female 19.00 0 0 330958 ## 46 46 0 3 Rogers, Mr. William John male NA 0 0 S.C./A.4. 23567 ## 47 47 0 3 Lennon, Mr. Denis male NA 1 0 370371 ## 48 48 1 3 O'Driscoll, Miss. Bridget female NA 0 0 14311 ## 49 49 0 3 Samaan, Mr. Youssef male NA 2 0 2662 ## 50 50 0 3 Arnold-Franchi, Mrs. Josef (Josefine Franchi) female 18.00 1 0 349237 ## 51 51 0 3 Panula, Master. Juha Niilo male 7.00 4 1 3101295 ## 52 52 0 3 Nosworthy, Mr. Richard Cater male 21.00 0 0 A/4. 39886 ## 53 53 1 1 Harper, Mrs. Henry Sleeper (Myna Haxtun) female 49.00 1 0 PC 17572 ## 54 54 1 2 Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson) female 29.00 1 0 2926 ## 55 55 0 1 Ostby, Mr. Engelhart Cornelius male 65.00 0 1 113509 ## 56 56 1 1 Woolner, Mr. Hugh male NA 0 0 19947 ## 57 57 1 2 Rugg, Miss. Emily female 21.00 0 0 C.A. 31026 ## 58 58 0 3 Novel, Mr. Mansouer male 28.50 0 0 2697 ## 59 59 1 2 West, Miss. Constance Mirium female 5.00 1 2 C.A. 34651 ## 60 60 0 3 Goodwin, Master. William Frederick male 11.00 5 2 CA 2144 ## 61 61 0 3 Sirayanian, Mr. Orsen male 22.00 0 0 2669 ## 62 62 1 1 Icard, Miss. Amelie female 38.00 0 0 113572 ## 63 63 0 1 Harris, Mr. Henry Birkhardt male 45.00 1 0 36973 ## 64 64 0 3 Skoog, Master. Harald male 4.00 3 2 347088 ## 65 65 0 1 Stewart, Mr. Albert A male NA 0 0 PC 17605 ## 66 66 1 3 Moubarek, Master. Gerios male NA 1 1 2661 ## 67 67 1 2 Nye, Mrs. (Elizabeth Ramell) female 29.00 0 0 C.A. 29395 ## 68 68 0 3 Crease, Mr. Ernest James male 19.00 0 0 S.P. 3464 ## 69 69 1 3 Andersson, Miss. Erna Alexandra female 17.00 4 2 3101281 ## 70 70 0 3 Kink, Mr. Vincenz male 26.00 2 0 315151 ## 71 71 0 2 Jenkin, Mr. Stephen Curnow male 32.00 0 0 C.A. 33111 ## 72 72 0 3 Goodwin, Miss. Lillian Amy female 16.00 5 2 CA 2144 ## 73 73 0 2 Hood, Mr. Ambrose Jr male 21.00 0 0 S.O.C. 14879 ## 74 74 0 3 Chronopoulos, Mr. Apostolos male 26.00 1 0 2680 ## 75 75 1 3 Bing, Mr. Lee male 32.00 0 0 1601 ## 76 76 0 3 Moen, Mr. Sigurd Hansen male 25.00 0 0 348123 ## 77 77 0 3 Staneff, Mr. Ivan male NA 0 0 349208 ## 78 78 0 3 Moutal, Mr. Rahamin Haim male NA 0 0 374746 ## 79 79 1 2 Caldwell, Master. Alden Gates male 0.83 0 2 248738 ## 80 80 1 3 Dowdell, Miss. Elizabeth female 30.00 0 0 364516 ## 81 81 0 3 Waelens, Mr. Achille male 22.00 0 0 345767 ## 82 82 1 3 Sheerlinck, Mr. Jan Baptist male 29.00 0 0 345779 ## 83 83 1 3 McDermott, Miss. Brigdet Delia female NA 0 0 330932 ## Fare Cabin Embarked ## 1 7.2500 S ## 2 71.2833 C85 C ## 3 7.9250 S ## 4 53.1000 C123 S ## 5 8.0500 S ## 6 8.4583 Q ## 7 51.8625 E46 S ## 8 21.0750 S ## 9 11.1333 S ## 10 30.0708 C ## 11 16.7000 G6 S ## 12 26.5500 C103 S ## 13 8.0500 S ## 14 31.2750 S ## 15 7.8542 S ## 16 16.0000 S ## 17 29.1250 Q ## 18 13.0000 S ## 19 18.0000 S ## 20 7.2250 C ## 21 26.0000 S ## 22 13.0000 D56 S ## 23 8.0292 Q ## 24 35.5000 A6 S ## 25 21.0750 S ## 26 31.3875 S ## 27 7.2250 C ## 28 263.0000 C23 C25 C27 S ## 29 7.8792 Q ## 30 7.8958 S ## 31 27.7208 C ## 32 146.5208 B78 C ## 33 7.7500 Q ## 34 10.5000 S ## 35 82.1708 C ## 36 52.0000 S ## 37 7.2292 C ## 38 8.0500 S ## 39 18.0000 S ## 40 11.2417 C ## 41 9.4750 S ## 42 21.0000 S ## 43 7.8958 C ## 44 41.5792 C ## 45 7.8792 Q ## 46 8.0500 S ## 47 15.5000 Q ## 48 7.7500 Q ## 49 21.6792 C ## 50 17.8000 S ## 51 39.6875 S ## 52 7.8000 S ## 53 76.7292 D33 C ## 54 26.0000 S ## 55 61.9792 B30 C ## 56 35.5000 C52 S ## 57 10.5000 S ## 58 7.2292 C ## 59 27.7500 S ## 60 46.9000 S ## 61 7.2292 C ## 62 80.0000 B28 ## 63 83.4750 C83 S ## 64 27.9000 S ## 65 27.7208 C ## 66 15.2458 C ## 67 10.5000 F33 S ## 68 8.1583 S ## 69 7.9250 S ## 70 8.6625 S ## 71 10.5000 S ## 72 46.9000 S ## 73 73.5000 S ## 74 14.4542 C ## 75 56.4958 S ## 76 7.6500 F G73 S ## 77 7.8958 S ## 78 8.0500 S ## 79 29.0000 S ## 80 12.4750 S ## 81 9.0000 S ## 82 9.5000 S ## 83 7.7875 Q ## [ reached 'max' / getOption("max.print") -- omitted 808 rows ] ``` ] --- ## A `tidyverse` / `readr` example ```r library(readr) titanic <- read_csv("./data/titanic.csv") ``` Please note the column specifications. `readr` 'guesses' them based on the first 1000 observations (we will come back to this later). --- class: middle .tinyish[ ```r titanic ``` ``` ## # A tibble: 891 x 12 ## PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked ## <dbl> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <chr> <chr> ## 1 1 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.25 <NA> S ## 2 2 1 1 Cumings, Mrs. John Bradley (Florence Brigg~ fema~ 38 1 0 PC 17599 71.3 C85 C ## 3 3 1 3 Heikkinen, Miss. Laina fema~ 26 0 0 STON/O2. 3101~ 7.92 <NA> S ## 4 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Pee~ fema~ 35 1 0 113803 53.1 C123 S ## 5 5 0 3 Allen, Mr. William Henry male 35 0 0 373450 8.05 <NA> S ## 6 6 0 3 Moran, Mr. James male NA 0 0 330877 8.46 <NA> Q ## 7 7 0 1 McCarthy, Mr. Timothy J male 54 0 0 17463 51.9 E46 S ## 8 8 0 3 Palsson, Master. Gosta Leonard male 2 3 1 349909 21.1 <NA> S ## 9 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmin~ fema~ 27 0 2 347742 11.1 <NA> S ## 10 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) fema~ 14 1 0 237736 30.1 <NA> C ## # ... with 881 more rows ``` ] It's that easy! --- ## A `readxl` example: `read_excel()` ```r library(readxl) unicorns <- read_xlsx("./data/observations.xlsx") ``` No output ☹️ --- class: middle ```r unicorns ``` ``` ## # A tibble: 42 x 3 ## countryname year pop ## <chr> <dbl> <dbl> ## 1 Austria 1670 85 ## 2 Austria 1671 83 ## 3 Austria 1674 75 ## 4 Austria 1675 82 ## 5 Austria 1676 79 ## 6 Austria 1677 70 ## 7 Austria 1678 81 ## 8 Austria 1680 80 ## 9 France 1673 70 ## 10 France 1674 79 ## # ... with 32 more rows ``` --- ## A `haven` example: `read_stata()` ```r library(haven) gp_covid <- read_stata("./data/ZA5667_v1-1-0_Stata14.dta") ``` --- class: middle ```r gp_covid ``` ``` ## za_number version doi id cohort sex age_cat education_cat intention_to_vote choice_of_party ## 1 ZA5667 v1-1-0 2020-04-27 10.4232/1.13520 1 3 1 7 3 2 1 ## 2 ZA5667 v1-1-0 2020-04-27 10.4232/1.13520 2 1 2 7 2 2 5 ## 3 ZA5667 v1-1-0 2020-04-27 10.4232/1.13520 3 3 1 8 2 2 1 ## 4 ZA5667 v1-1-0 2020-04-27 10.4232/1.13520 4 2 1 4 3 2 1 ## 5 ZA5667 v1-1-0 2020-04-27 10.4232/1.13520 5 1 2 1 3 -33 -33 ## 6 ZA5667 v1-1-0 2020-04-27 10.4232/1.13520 6 1 1 10 2 2 6 ## 7 ZA5667 v1-1-0 2020-04-27 10.4232/1.13520 7 1 2 4 2 2 6 ## political_orientation marstat household hzcy001a hzcy002a hzcy003a hzcy004a hzcy005a hzcy006a hzcy007a hzcy008a hzcy009a hzcy010a ## 1 6 2 1 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 ## 2 5 1 2 5 5 2 5 5 1 0 0 0 0 ## 3 5 1 2 5 6 3 6 6 1 1 0 0 0 ## 4 7 1 3 4 4 2 4 3 0 0 1 0 0 ## 5 4 2 3 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 ## 6 10 1 2 3 3 -99 3 3 1 0 0 0 0 ## 7 5 1 3 4 3 3 3 4 1 1 0 0 0 ## hzcy011a hzcy012a hzcy013a hzcy014a hzcy015a hzcy016a hzcy018a hzcy019a hzcy020a hzcy021a hzcy022a hzcy023a hzcy024a hzcy025a ## 1 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 ## 2 1 1 1 0 0 0 0 5 5 5 5 5 5 5 ## 3 1 0 0 1 0 0 0 5 5 5 4 5 5 4 ## 4 1 1 0 1 0 0 0 3 4 4 3 4 2 2 ## 5 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 ## 6 0 1 0 0 0 0 0 4 4 4 4 4 2 3 ## 7 1 0 0 1 0 0 0 5 5 5 5 5 5 3 ## hzcy026a hzcy027a hzcy028a hzcy029a hzcy030a hzcy031a hzcy032a hzcy033a hzcy034a hzcy035a hzcy036a hzcy037a hzcy038a hzcy039a ## 1 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 ## 2 4 -88 -88 -88 -88 -88 -88 -88 -88 -88 -88 -88 -88 -88 ## 3 1 5 1 4 5 5 5 -88 -88 -88 -88 -88 -88 -88 ## 4 1 4 2 3 3 3 3 -88 -88 -88 -88 -88 -88 -88 ## 5 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 ## 6 1 5 2 4 4 4 4 -88 -88 -88 -88 -88 -88 -88 ## 7 1 5 3 5 5 5 5 -88 -88 -88 -88 -88 -88 -88 ## hzcy040a hzcy041a hzcy042a hzcy043a hzcy044a hzcy045a hzcy046a hzcy047a hzcy048a hzcy049a hzcy050a hzcy051a hzcy052a hzcy053a ## 1 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 ## 2 3 3 3 3 5 4 4 5 4 4 4 4 4 1 ## 3 2 3 3 3 4 4 5 5 4 3 4 2 5 5 ## 4 3 4 3 3 4 4 4 4 4 4 4 4 4 1 ## 5 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 ## 6 2 3 3 2 4 5 4 5 4 2 4 3 5 5 ## 7 3 3 3 3 4 4 4 4 3 3 3 2 4 1 ## hzcy054a hzcy055a hzcy056a hzcy057a hzcy058a hzcy059a hzcy060a hzcy061a hzcy062a hzcy063a hzcy064a hzcy065a hzcy066a hzcy067a ## 1 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 ## 2 0 1 0 0 0 0 0 -88 -88 -88 -88 -88 -88 -88 ## 3 -88 -88 -88 -88 -88 -88 -88 -88 -88 -88 -88 -88 -88 -88 ## 4 0 0 0 0 0 0 1 -88 -88 -88 -88 -88 -88 -88 ## 5 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 ## 6 -88 -88 -88 -88 -88 -88 -88 -88 -88 -88 -88 -88 -88 -88 ## 7 0 0 0 0 0 0 1 -88 -88 -88 -88 -88 -88 -88 ## hzcy068a hzcy069a hzcy070a hzcy071a hzcy072a hzcy073a hzcy074a hzcy075a hzcy076a hzcy077a hzcy078a hzcy079a hzcy080a hzcy081a ## 1 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 ## 2 -88 -88 -88 2 -88 -88 -88 -88 -88 -88 -88 -88 -88 -88 ## 3 -88 -88 -88 2 -88 -88 -88 -88 -88 -88 -88 -88 -88 -88 ## 4 -88 -88 -88 1 1 0 0 0 0 0 0 0 0 0 ## 5 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 ## 6 -88 -88 -88 2 -88 -88 -88 -88 -88 -88 -88 -88 -88 -88 ## 7 -88 -88 -88 1 1 1 0 0 0 0 0 0 1 0 ## hzcy083a hzcy084a hzcy085a hzcy086a hzcy087a hzcy088a hzcy089a hzcy090a hzcy091a hzcy092a hzcy093a hzcy095a hzcy096a hzcy097a ## 1 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 ## 2 -88 1 1 0 0 0 1 1 0 0 1 0 4 0 ## 3 -88 1 1 0 1 0 1 0 0 1 0 0 -88 -88 ## 4 0 1 0 0 0 0 0 0 0 0 0 0 -88 -88 ## 5 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 ## 6 -88 1 0 0 0 0 1 0 0 0 0 0 -88 -88 ## 7 0 0 1 0 0 0 0 0 0 0 0 0 -88 -88 ## hzcy098a hzcy099a hzza001a hzza002a hzza003a hzzq009a hzzq016b hzzq023a hzzp201a hzzp204a hzzp207a hzzr001a hzzr002a hzzr003a ## 1 -33 -33 1 0 0 -33 -33 -33 -33 -33 -33 -33 -33 -33 ## 2 0 1 1 1 1 4 0 5 31 210 1584549879 3 24 48 ## 3 -88 -88 1 1 1 5 0 5 31 377 1584469614 34 83 117 ## 4 -88 -88 1 1 1 4 0 4 31 309 1584525461 4 35 67 ## 5 -33 -33 1 0 0 -33 -33 -33 -33 -33 -33 -33 -33 -33 ## 6 -88 -88 1 1 1 4 0 4 31 429 1584461540 3 41 90 ## 7 -88 -88 1 1 1 3 0 4 31 586 1584823080 7 67 121 ## hzzr004a hzzr005a hzzr006a hzzr007a hzzr008a hzzr009a hzzr010a hzzr011a hzzr012a hzzr013a hzzr014a hzzr015a hzzr016a hzzr017a ## 1 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 ## 2 71 82 0 0 101 130 145 150 0 189 193 200 209 210 ## 3 161 175 206 0 245 293 0 312 0 345 0 0 360 377 ## 4 101 110 140 0 166 190 216 222 246 266 0 0 307 309 ## 5 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 -33 ## 6 143 159 209 0 288 340 0 366 0 412 0 0 424 429 ## 7 212 230 264 0 319 388 438 446 509 558 0 0 576 586 ## hzzr018a hzzr019a ## 1 -33 -33 ## 2 138 0 ## 3 307 0 ## 4 206 0 ## 5 -33 -33 ## 6 360 0 ## 7 416 0 ## [ reached 'max' / getOption("max.print") -- omitted 3758 rows ] ``` --- ## `read_stata()`'s sister: `read_spss()` Indeed, there's also the function `read_spss()` to import *SPSS* files. It also provides capabilities to handle *SPSS*-defined missing values by setting the option `user_na = TRUE` (default is `FALSE`). *Note*: The [`sjlabelled` package](https://cran.r-project.org/web/packages/sjlabelled/index.html) can also be used to choose a more elaborated approach for missing values: https://cran.r-project.org/web/packages/sjlabelled/vignettes/intro_sjlabelled.html **We will come back to Stata and SPSS files since they depict a specific file format in `R`: labelled data.** --- ## There's more These were just some very first examples of applying functions from each package. They comprise even more functions for different data types. pull-left[ `readr` - `read_csv()` - `read_tsv()` - `read_delim()` - `read_fwf()` - `read_table()` - `read_log()` ] pull-right[ `haven` - `read_sas()` - `read_spss()` - `read_stata()` ] Not to mention all the helper functions and options. For example, we can define the cells to read from an *Excel* file by specifying the option `range = "C1:E4"` in `read_excel()` --- ## Data type specifications for `tibbles` - characters - indicated by `<chr>` - specified by `col_character()` - integers - indicated by `<int>` - specified by `col_integer()` - doubles - indicated by `<dbl>` - specified by `col_double()` - factors - indicated by `<fct>` - specified by `col_factor()` - logical - indicated by `<lgl>` - specified by `col_logical()` --- ## Changing variable types, e.g., in CSV files As mentioned before, `read_csv` 'guesses' the variable types by scanning the first 1000 observations. **NB**: This can go wrong! Luckily, we can change the variable type... - before/while loading the data - and after loading the data --- ## While loading the data in `read_csv` ```r titanic <- read_csv( "./data/titanic.csv", col_types = cols( PassengerId = col_double(), Survived = col_double(), Pclass = col_double(), Name = col_character(), Sex = col_character(), Age = col_double(), SibSp = col_double(), Parch = col_double(), Ticket = col_character(), Fare = col_double(), Cabin = col_character(), Embarked = col_character() ) ) titanic ``` .right[↪️] --- class: middle ``` ## # A tibble: 891 x 12 ## PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked ## <dbl> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <chr> <chr> ## 1 1 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.25 <NA> S ## 2 2 1 1 Cumings, Mrs. John Bradley (Florence Brigg~ fema~ 38 1 0 PC 17599 71.3 C85 C ## 3 3 1 3 Heikkinen, Miss. Laina fema~ 26 0 0 STON/O2. 3101~ 7.92 <NA> S ## 4 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Pee~ fema~ 35 1 0 113803 53.1 C123 S ## 5 5 0 3 Allen, Mr. William Henry male 35 0 0 373450 8.05 <NA> S ## 6 6 0 3 Moran, Mr. James male NA 0 0 330877 8.46 <NA> Q ## 7 7 0 1 McCarthy, Mr. Timothy J male 54 0 0 17463 51.9 E46 S ## 8 8 0 3 Palsson, Master. Gosta Leonard male 2 3 1 349909 21.1 <NA> S ## 9 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmin~ fema~ 27 0 2 347742 11.1 <NA> S ## 10 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) fema~ 14 1 0 237736 30.1 <NA> C ## # ... with 881 more rows ``` --- ## While loading the data in `read_csv` ```r titanic <- read_csv( "./data/titanic.csv", col_types = cols( PassengerId = col_double(), Survived = col_double(), Pclass = col_double(), Name = col_character(), Sex = col_factor(), # This one changed! Age = col_double(), SibSp = col_double(), Parch = col_double(), Ticket = col_character(), Fare = col_double(), Cabin = col_character(), Embarked = col_character() ) ) titanic ``` .right[↪️] --- class: middle .tinyisher[ ``` ## # A tibble: 891 x 12 ## PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked ## <dbl> <dbl> <dbl> <chr> <fct> <dbl> <dbl> <dbl> <chr> <dbl> <chr> <chr> ## 1 1 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.25 <NA> S ## 2 2 1 1 Cumings, Mrs. John Bradley (Florence Brigg~ fema~ 38 1 0 PC 17599 71.3 C85 C ## 3 3 1 3 Heikkinen, Miss. Laina fema~ 26 0 0 STON/O2. 3101~ 7.92 <NA> S ## 4 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Pee~ fema~ 35 1 0 113803 53.1 C123 S ## 5 5 0 3 Allen, Mr. William Henry male 35 0 0 373450 8.05 <NA> S ## 6 6 0 3 Moran, Mr. James male NA 0 0 330877 8.46 <NA> Q ## 7 7 0 1 McCarthy, Mr. Timothy J male 54 0 0 17463 51.9 E46 S ## 8 8 0 3 Palsson, Master. Gosta Leonard male 2 3 1 349909 21.1 <NA> S ## 9 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmin~ fema~ 27 0 2 347742 11.1 <NA> S ## 10 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) fema~ 14 1 0 237736 30.1 <NA> C ## # ... with 881 more rows ``` ] --- ## After loading the data ```r titanic <- type_convert( titanic, col_types = cols( PassengerId = col_double(), Survived = col_double(), Pclass = col_double(), Name = col_character(), Sex = col_factor(), Age = col_double(), SibSp = col_double(), Parch = col_double(), Ticket = col_character(), Fare = col_double(), Cabin = col_character(), Embarked = col_character() ) ) ``` --- ## Beyond flat files: labelled data A lot of data you get and find or even collect comes in some sort of flat file format, such as CSV. In the social sciences, however, we often deal with proprietary file formats, such as *SPSS*'s `.sav` or *Stata*'s `.dta` files. What we often find in these data are labels. These labels are used to describe variables or variable values. They comprise some specific metadata inherent in these proprietary file formats. *If you were able to travel ten years back in time and ask an `R` geek, she'd say that you cannot use labels in R. You'd either have to import, e.g., value labels as character strings or use their codes as factors. However, these days...* --- ## Not being able to use labelled data is a thing of the past Nowadays, if you use the `haven` package, labels are built-in. For example: ```r gp_covid <- haven::read_sav("./data/ZA5667_v1-1-0.sav") gp_covid["age_cat"] ``` ``` ## # A tibble: 3,765 x 1 ## age_cat ## <dbl+lbl> ## 1 7 [51 bis 60 Jahre] ## 2 7 [51 bis 60 Jahre] ## 3 8 [61 bis 65 Jahre] ## 4 4 [36 bis 40 Jahre] ## 5 1 [<=25 Jahre] ## 6 10 [>=71 Jahre] ## 7 4 [36 bis 40 Jahre] ## 8 7 [51 bis 60 Jahre] ## 9 8 [61 bis 65 Jahre] ## 10 1 [<=25 Jahre] ## # ... with 3,755 more rows ``` --- ## Advantages of using labelled data One could rejoice in not having to use a codebook any more, just like in *SPSS*. And I think by and large this is true, although just looking at code output for glimpsing at data is somewhat... geeky. An advantage is definitely that you could re-use the labels in figures and plots, some packages do that automatically, such as the [`sjPlot`](https://strengejacke.github.io/sjPlot/) package. Yet, primarily when you exchange your data with colleagues who do not use `R` or when you plan to publish your data (which you always should if that is possible), being able to export data you have manipulated in `R` is great. **However, be aware of the missing values hell that you may have to enter due to different missing values definitions in Stata and SPSS.** --- ## Getting labels For variables. ```r sjlabelled::get_label(gp_covid$age_cat) ``` ``` ## [1] "Alter, kategorisiert" ``` For values. .tinyish[ ```r sjlabelled::get_labels(gp_covid$age_cat) ``` ``` ## [1] "<=25 Jahre" "26 bis 30 Jahre" "31 bis 35 Jahre" "36 bis 40 Jahre" "41 bis 45 Jahre" "46 bis 50 Jahre" "51 bis 60 Jahre" ## [8] "61 bis 65 Jahre" "66 bis 70 Jahre" ">=71 Jahre" ``` ] --- ## And setting labels: Variables ```r gp_covid$age_cat <- sjlabelled::set_label(gp_covid$age_cat, label = "Age, categorized") sjlabelled::get_label(gp_covid$age_cat) ``` ``` ## [1] "Age, categorized" ``` --- ## And setting labels: Values .tinyish[ ```r gp_covid$age_cat <- sjlabelled::set_labels( gp_covid$age_cat, labels = c( "<=25 years", "26 to 30 years", "31 to 35 years", "36 to 40 years", "41 to 45 years", "46 to 50 years", "51 to 60 years", "61 to 65 years", "66 to 70 years", ">=71 years" ) ) sjlabelled::get_labels(gp_covid$age_cat) ``` ``` ## [1] "<=25 years" "26 to 30 years" "31 to 35 years" "36 to 40 years" "41 to 45 years" "46 to 50 years" "51 to 60 years" ## [8] "61 to 65 years" "66 to 70 years" ">=71 years" ``` ] --- ## That's a lot of manual work Yeah, this requires some tedious manual work that has to be done, at least by somebody. But that's just how it is, even in *SPSS* or *Stata*. Indeed, we may want to wait until using it in `R` scales a bit more. Integrating basic labelling of variables in a pipe workflow, however, is already straightforward: ```r gp_data_subset <- gp_covid %>% dplyr::select(age_cat, sex) %>% sjlabelled::var_labels( age_cat = "Age in Categories", sex = "Gender in Binary Form" ) sjlabelled::get_label(gp_data_subset) ``` ``` ## age_cat sex ## "Age in Categories" "Gender in Binary Form" ``` Ok, but that's already data wrangling, a topic for tomorow. --- class: center, middle # [Exercise](https://jobreu.github.io/r-intro-gesis-2021/exercises/Exercise_1_2_2_Flat_Files.html) time 🏋️‍♀️💪🏃🚴 ## [Solutions](https://jobreu.github.io/r-intro-gesis-2021/solutions/Exercise_1_2_2_Flat_Files.html) --- ## Exporting data Sometimes our data have to leave `R`, for example, if we.... - share data with colleagues who do not use `R` - want to continue where we left off - particularly if data wrangling took a long time For such purposes we also need a way to export our data. All of the packages we have discussed in this session also have designated functions for that. <img src="data:image/png;base64,#C:\Users\breuerjs\Documents\Lehre\r-intro-gesis-2021\content\img\export_data.png" width="50%" style="display: block; margin: auto;" /> --- ## Examples: CSV and Stata files ```r write_csv(titanic, "titanic_own.csv") ``` ```r write_dta(titanic, "titanic_own.dta") ``` --- ## `R`'s native file formats If you plan to continue to work with `R` (something we would always recommend 😜), there are at least two native 'file formats' to choose from. The advantage of using them is that they are compressed files, so that they don't occupy unnecessarily large disk space. Moreover, they are already prepared as you left them and take less time to load (not a big deal in a small data world but relevant for big(ger) data). `.Rdata`/`.rda` files saving and loading: ```r save(mydata, file = "mydata.RData") load("mydata.RData") ``` `.rds` files saving and loading. ```r saveRDS(mydata, "mydata.rds") mydata <- readRDS("mydata.rds") ``` `saveRDS()` just saves a representation of the object, which means you can name it whatever you want when loading. --- ## Saving just everything If you have not changed the General Global Options in *RStudio* as suggested in the *Getting Started* session, you may have noticed that, when closing *Rstudio*, by default, the programs asks you whether you want to save the workspace image. <img src="data:image/png;base64,#C:\Users\breuerjs\Documents\Lehre\r-intro-gesis-2021\content\img\save_image.png" width="50%" style="display: block; margin: auto;" /> You can also do that whenever you want using the `save.image()` function: ```r save.image(file = "my_fancy_workspace.RData") ``` .small[ *Note*: As we've said before, though, this is not something we'd recommend as a worfklow. Instead, you should (explicitly and separately) save your `R` scripts and data sets (in appropriate formats). ] --- ## Additional packages The great benefit of `tidyverse` import functions is the import of the data as tibbles: the data are potentially tidier. Several other non-tidyverse packages provide similar benefits as they make use of this universal data format: - [`sf`](https://github.com/r-spatial/sf) for geospatial data - [`sjlabelled`](https://cran.r-project.org/web/packages/sjlabelled/index.html) to work with labelled data, e.g., from *SPSS* or *Stata* --- ## Other packages for data import - `base` R - the [`foreign` package](https://cran.r-project.org/web/packages/foreign/index.html) for *SPSS* and *Stata* files - [`data.table`](https://cran.r-project.org/web/packages/data.table/index.html) or [`fst`](https://www.fstpackage.org/) for large datasets - [`jsonlite`](https://cran.r-project.org/web/packages/jsonlite/index.html) for `.json` files - [`datapasta`](https://github.com/MilesMcBain/datapasta) for copying and pasting data into tribbles (e.g., from websites, *Excel* or *Word* files) --- ## Final note on file paths There is this simple rule of never using absolute file paths to maintain your code reproducibly and future-proof. We already talked about this in the introduction, so it's just to remind you as this is particularly important for data importing and exporting. ```r # Windows load("C:/Users/cool_user/data/fancy_data.Rdata") # Mac load("/Users/cool_user/data/fancy_data.Rdata") # GNU/Linux load("/home/cool_user/data/fancy_data.Rdata") ``` --- ## Use relative paths Instead of using absolute paths, it is recommended to use relative file paths. The general principle here is to start from a directory where your current script currently exists and navigate to your target location. Say we are in the "C:/Users/cool_user/" location on a Windows machine. To load your data, we would use: ```r load("./data/fancy_data.Rdata") ``` If we were in a different folder, e.g., "C:/Users/cool_user/cat_pics/mittens/", we would use: ```r load("../../data/fancy_data.Rdata") ``` --- class: center, middle Please first download the [Public Use File (PUF) of the GESIS Panel Special Survey on the Coronavirus SARS-CoV-2 Outbreak in Germany](https://search.gesis.org/research_data/ZA5667) as .sav, .dta, and .csv file. # [Exercise](https://jobreu.github.io/r-intro-gesis-2021/exercises/Exercise_1_2_3_Statistical_Software_Files.html) time 🏋️‍♀️💪🏃🚴 ## [Solutions](https://jobreu.github.io/r-intro-gesis-2021/solutions/Exercise_1_2_3_Statistical_Software_Files.html)